根据工人动作动态判断防护装备是否合规，显著减少误报。
Action Recognition based Industrial Safety Violation Detection
- 先识别工人动作，再匹配对应防护要求
- 在109个视频上实现F1分数提升23%
- 适合需要精准安全监控的制造场景
正确使用个人防护装备（PPE）可挽救工业工人生命，是计算机视觉在大型制造业中的广泛应用。然而，现有部署系统常产生大量误报，因其将PPE要求泛化到全行业和所有任务。解决该问题的关键在于理解工人的具体动作，并针对该动作定制PPE检查逻辑。本文提出一种系统：首先使用动作识别模型解析工人正在执行的动作，再通过目标检测技术核查是否存在违规。在包含109个视频的测试数据集上，该方法相比传统的PPE检测方法，F1分数提升了23%。
原文摘要 · Abstract (English)
Proper use of personal protective equipment (PPE) can save the lives of industry workers and it is a widely used application of computer vision in the large manufacturing industries. However, most of the applications deployed generate a lot of false alarms (violations) because they tend to generalize the requirements of PPE across the industry and tasks. The key to resolving this issue is to understand the action being performed by the worker and customize the inference for the specific PPE requirements of that action. In this paper, we propose a system that employs activity recognition models to first understand the action being performed and then use object detection techniques to check for violations. This leads to a 23% improvement in the F1-score compared to the PPE-based approach on our test dataset of 109 videos.
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